Examining the effect of MS on brain volumes in a pediatric sample
Virgilio Gonzenbach
2023-01-13
MSKIDS analysis
This report examines the effect of Multiple Sclerosis (MS) on brain volumes in a pediatric sample. The data used in this analysis consists of a harmonized dataset of volumetric measurements of 145 brain regionm and covariates such as age, sex and MS status.
Multiple linear regression models were run to investigate the relationship between MS and brain volumes. The models were run separately for each brain region of interest (ROI) and the results were reported for ROIs that showed significant associations with MS.
library(tidyverse)
library(broom)
library(shiny)
# load the data
df <- read.csv('data/harmonized_data_PNC+MS.csv', stringsAsFactors = TRUE) %>%
mutate(MS = as.factor(MS)) %>%
mutate_at(vars(starts_with('X')), ~ . *0.001) %>% # convert mm^3 to cm^3
rename(ICV = X702,
Sex = sex,
Age = age)
# capture roi indices as vector for functions
ROI_INDICES <- df %>%
select(starts_with("X")) %>%
colnames()
# load the ROI dictionary
dict_df <- readxl::read_excel('data/MUSE_ROI_Dict.xlsx') %>%
filter(ROI_NAME == 'ICV' | ROI_INDEX < 300) %>%
select(ROI_INDEX, TISSUE_SEG, ROI_NAME) %>%
mutate(ROI_INDEX = paste0('X', ROI_INDEX),
TISSUE_SEG = recode(TISSUE_SEG, `GM+WM+VN+BS+cCSF` = 'ICV')) %>%
filter(ROI_INDEX %in% ROI_INDICES)Sample size
# print cell sizes and total sample size
df %>%
count(Sex, MS) %>%
bind_rows(summarize(., n = sum(n)))## Sex MS n
## 1 FEMALE 0 696
## 2 FEMALE 1 50
## 3 MALE 0 590
## 4 MALE 1 17
## 5 <NA> <NA> 1353Models
Multiple regression models were run to investigate the relationship between MS and brain volumes. The models were run separately for each brain region of interest (ROI) and the results were reported for ROIs that showed significant associations with MS. The models controlled for sex, age, age squared and intracranial volume (ICV). Additionally, interactions between MS and sex were also included in the model. The results of these models are reported and visualized using box plots to help interpret the findings.
The analysis shows that MS is associated with specific brain volume changes in pediatric population.
Model notation:
\[\begin{align} ROI &= \beta_0 + \beta_1*Sex + \beta_2*Age + \beta_3*Age^2 + \beta_4*Sex*Age + \beta_5*MS*Sex + \beta_6*ICV + \epsilon \end{align}\]
Model R formula:
ROI ~ Sex + Age + Age^2 + Sex*Age + MS*Sex + ICV
# No MS-sex interation Model: ROI ~ Sex + Age + Age^2 + Sex\*Age + MS + ICV
run_model <- function(outcome){
#' run model for each ROI
f <- as.formula(sprintf("%s ~ Sex + Age + Age^2 + Sex*Age + MS + ICV", outcome))
res <- lm(f, data = df)
}
# run models searately
models <- ROI_INDICES %>%
purrr::map(run_model) %>%
purrr::map(tidy) %>%
setNames(ROI_INDICES) %>%
bind_rows(.id = 'ROI_INDEX') %>%
left_join(dict_df, by = c('ROI_INDEX')) %>% # join with ROI dictionary
select(ROI_INDEX, ROI_NAME, TISSUE_SEG, everything())
# use FDR correction
models %>%
filter(term == 'MS1') %>%
mutate(p.value = p.adjust(p.value, method='fdr')) %>%
filter(p.value < 0.05)run_model <- function(outcome){
#' run model for one ROI
f <- as.formula(sprintf("%s ~ Sex + Age + Age^2 + Sex*Age + MS*Sex + ICV", outcome))
res <- lm(f, data = df)
}
# run models searately for each ROI
models <- ROI_INDICES %>%
purrr::map(run_model) %>%
purrr::map(tidy) %>%
setNames(ROI_INDICES) %>%
bind_rows(.id = 'ROI_INDEX') %>%
left_join(dict_df, by = c('ROI_INDEX')) %>%
select(ROI_INDEX, ROI_NAME, TISSUE_SEG, everything())
# select effects/terms of interest and adjust p values. keep significant effects
sig_effects <- models %>%
filter(term %in% c('SexMALE:MS1', 'MS1')) %>%
mutate(p.value = p.adjust(p.value, method='fdr')) %>%
filter(p.value < 0.05) %>%
arrange(by = TISSUE_SEG)Significant main effects: MS
With healthy controls as the reference group, the main effect (in
\(cm^3\)) of having MS is shown in the
estimate column for each significant ROI.
sig_effects %>%
filter(term == 'MS1')## # A tibble: 9 × 8
## ROI_INDEX ROI_NAME TISSU…¹ term estim…² std.e…³ stati…⁴ p.value
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 X59 Right Thalamus Proper GM MS1 -0.659 0.0675 -9.77 1.14e-19
## 2 X60 Left Thalamus Proper GM MS1 -0.680 0.0691 -9.85 1.10e-19
## 3 X107 Left AnG angular g… GM MS1 -0.778 0.207 -3.77 6.21e- 3
## 4 X121 Left FRP frontal p… GM MS1 -0.228 0.0730 -3.12 4.05e- 2
## 5 X122 Right FuG fusiform… GM MS1 -0.452 0.142 -3.19 3.80e- 2
## 6 X139 Left MCgG middle ci… GM MS1 -0.388 0.0936 -4.15 1.49e- 3
## 7 X4 3rd Ventricle VN MS1 0.153 0.0312 4.89 6.50e- 5
## 8 X81 frontal lobe WM right WM MS1 2.33 0.709 3.29 3.02e- 2
## 9 X82 frontal lobe WM left WM MS1 3.10 0.699 4.44 4.68e- 4
## # … with abbreviated variable names ¹TISSUE_SEG, ²estimate, ³std.error,
## # ⁴statisticResults show that across Grey Matter (GM) regions, MS patients show decreased volumes relative to controls; whereas in the \(3^{rd}\) ventricle and the left and right frontal lobes, MS patients had higher volume than controls.
Significant interaction effects
With MS females as the reference group, the interaction effect (in
\(cm^3\)) of having MS and being male
is shown in the estimate column for each significant ROI.
These effects are independent of the main effect of sex.
sig_effects %>%
filter(term == 'SexMALE:MS1')## # A tibble: 4 × 8
## ROI_INDEX ROI_NAME TISSUE…¹ term estim…² std.e…³ stati…⁴ p.value
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 X59 Right Thalamus Proper GM SexM… 0.812 0.132 6.17 6.47e-8
## 2 X60 Left Thalamus Proper GM SexM… 0.893 0.135 6.63 4.81e-9
## 3 X49 Right Inf Lat Vent VN SexM… 0.121 0.0324 3.73 6.39e-3
## 4 X82 frontal lobe WM left WM SexM… -4.28 1.36 -3.14 4.05e-2
## # … with abbreviated variable names ¹TISSUE_SEG, ²estimate, ³std.error,
## # ⁴statisticResults that show that MS males have higher volumes than females in right and left Thalamus and right inferior lateral ventricle. On the other hand MS males exhibit reduced volumes in the left frontal lobe than MS females.
Visualization
The means for the ROIs with a significant main effect (of MS status) and/or interaction (of MS status with sex) are plotted below by age and MS status.
plot_means <- function(roi, data=df) {
#' make a box plot for a given roi
roi_name <- dict_df %>%
filter(ROI_INDEX == roi) %>%
pull(ROI_NAME)
sig_effects <- sig_effects %>%
filter(ROI_INDEX == roi) %>%
pull(term) %>%
recode("MS1" = "main effect", "SexMALE:MS1" = "interaction")
title <- ggtitle(sprintf("Significant effects of MS: %s", knitr::combine_words(sig_effects)))
data %>%
mutate(MS=fct_recode(MS, Yes = "1", No = "0")) %>%
ggplot(aes_string(y=roi, x='MS', fill='Sex')) +
geom_boxplot() +
ylab(sprintf("%s (cm3)", roi_name)) +
theme_bw() + title
}
## make a boxplot for ROIs with significant effects
sig_rois <- sig_effects %>%
pull(ROI_INDEX) %>%
unique()
plots <- sig_rois %>%
purrr::map(~ plot_means(.x), data=df) %>%
setNames(sig_rois)Volumes
X59
X60
X107
X121
X122
X139
X4
X49
X81
X82
Volumes minus Age and ICV effect
# regress out nuisance effects from roi
reg_out_roi <- function(roi){
#' run model for each ROI
model <- lm(roi ~ Age + Age^2 + ICV, data = df)
model$residuals
}
reg_out_df <- df %>%
mutate_at(vars(starts_with('X')), reg_out_roi)
plots_reg_out <- sig_rois %>%
purrr::map(plot_means, data=reg_out_df) %>%
setNames(sig_rois)X59
X60
X107
X121
X122
X139
X4
X49
X81
X82
Distribution of control variables
Age
df$Age %>% summary() %>% as.matrix() %>% t() %>% as.data.frame()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 5.69 12.16667 15.58333 15.36221 18.33333 29.4df %>%
mutate(MS=fct_recode(MS, Yes = "1", No = "0")) %>%
ggplot(aes(y=Age, fill=Sex, x = MS)) + geom_boxplot() + theme_bw()
ICV
df$ICV %>% summary() %>% as.matrix() %>% t() %>% as.data.frame()## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1032.233 1317.867 1411.6 1419.895 1516.664 1872.69df %>%
mutate(MS=fct_recode(MS, Yes = "1", No = "0")) %>%
ggplot(aes(y=ICV, fill=Sex, x = MS)) + geom_boxplot() + theme_bw() + ylab('ICV (cm3)') 